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https://github.com/microsoft/qlib.git
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Add TabNet config
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Jactus
@@ -197,7 +197,8 @@ Here is a list of models built on `Qlib`.
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- [GRU based on pytorch](qlib/contrib/model/pytorch_gru.py)
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- [LSTM based on pytorcn](qlib/contrib/model/pytorch_lstm.py)
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- [GATs based on pytorch](qlib/contrib/model/pytorch_gats.py)
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- [TFT based on tensorflow-1.15.0](examples/benchmarks/TFT/tft.py)
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- [TabNet based on pytorch](qlib/contrib/model/tabnet.py)
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<!-- - [TFT based on tensorflow](examples/benchmarks/TFT/tft.py) -->
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Your PR of new Quant models is highly welcomed.
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@@ -37,7 +37,7 @@ task:
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module_path: qlib.data.dataset
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kwargs:
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handler:
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class: ALPHA360_Denoise
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class: Alpha158
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module_path: qlib.contrib.data.handler
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kwargs: *data_handler_config
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segments:
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@@ -44,7 +44,7 @@ task:
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module_path: qlib.data.dataset
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kwargs:
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handler:
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class: ALPHA360_Denoise
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class: Alpha158
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module_path: qlib.contrib.data.handler
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kwargs: *data_handler_config
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segments:
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5
examples/benchmarks/TabNet/requirements.txt
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5
examples/benchmarks/TabNet/requirements.txt
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@@ -0,0 +1,5 @@
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pandas==1.1.2
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numpy==1.17.4
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scikit_learn==0.23.2
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torch==1.7.0
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pytorch-tabnet==2.0.1
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66
examples/benchmarks/TabNet/workflow_config_tabnet.yaml
Normal file
66
examples/benchmarks/TabNet/workflow_config_tabnet.yaml
Normal file
@@ -0,0 +1,66 @@
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provider_uri: "~/.qlib/qlib_data/cn_data"
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region: cn
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market: &market csi300
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benchmark: &benchmark SH000300
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data_handler_config: &data_handler_config
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start_time: 2008-01-01
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end_time: 2020-08-01
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fit_start_time: 2008-01-01
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fit_end_time: 2014-12-31
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instruments: *market
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port_analysis_config: &port_analysis_config
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strategy:
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class: TopkDropoutStrategy
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module_path: qlib.contrib.strategy.strategy
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kwargs:
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topk: 50
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n_drop: 5
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backtest:
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verbose: False
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limit_threshold: 0.095
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account: 100000000
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benchmark: *benchmark
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deal_price: close
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open_cost: 0.0005
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close_cost: 0.0015
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min_cost: 5
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task:
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model:
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class: TabNetModel
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module_path: qlib.contrib.model.tabnet
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kwargs:
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n_d: 8
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n_a: 8
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n_steps: 3
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gamma: 1.3
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n_independent: 2
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n_shared: 2
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seed: 0
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momentum: 0.02
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lambda_sparse: 1e-3
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optimizer_params: {lr: 2e-3}
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dataset:
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class: DatasetH
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module_path: qlib.data.dataset
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kwargs:
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handler:
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class: Alpha158
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module_path: qlib.contrib.data.handler
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kwargs: *data_handler_config
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segments:
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train: [2008-01-01, 2014-12-31]
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valid: [2015-01-01, 2016-12-31]
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test: [2017-01-01, 2020-08-01]
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record:
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- class: SignalRecord
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module_path: qlib.workflow.record_temp
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kwargs: {}
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- class: SigAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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ana_long_short: False
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ann_scaler: 252
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- class: PortAnaRecord
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module_path: qlib.workflow.record_temp
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kwargs:
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config: *port_analysis_config
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@@ -71,7 +71,7 @@ if __name__ == "__main__":
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"seed": 0,
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"momentum": 0.02,
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"lambda_sparse": 1e-3,
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"optimizer_params": {'lr':2e-3}
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"optimizer_params": {"lr": 2e-3},
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},
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},
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"dataset": {
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@@ -9,19 +9,24 @@ from ...model.base import Model
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from ...data.dataset import DatasetH
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from ...data.dataset.handler import DataHandlerLP
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class TabNetModel(Model):
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"""TabNetModel Model"""
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def __init__(self, n_d, n_a,
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n_steps,
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gamma,
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n_independent,
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n_shared,
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seed,
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momentum,
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lambda_sparse,
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optimizer_params,
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**kwargs):
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def __init__(
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self,
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n_d,
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n_a,
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n_steps,
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gamma,
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n_independent,
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n_shared,
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seed,
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momentum,
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lambda_sparse,
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optimizer_params,
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**kwargs
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):
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self.model = None
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self.n_d = n_d
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@@ -47,28 +52,28 @@ class TabNetModel(Model):
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seed=0,
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momentum=0.02,
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lambda_sparse=1e-3,
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optimizer_params={'lr':2e-3},
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optimizer_params={"lr": 2e-3},
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**kwargs
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):
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df_train, df_valid = dataset.prepare(
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["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
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)
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x_train, y_train = df_train["feature"].values, df_train["label"].values*100
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x_valid, y_valid = df_valid["feature"].values, df_valid["label"].values*100
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x_train, y_train = df_train["feature"].values, df_train["label"].values * 100
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x_valid, y_valid = df_valid["feature"].values, df_valid["label"].values * 100
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self.model = TabNetRegressor(
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n_d=self.n_d,
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n_a=self.n_a,
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n_steps=self.n_steps,
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gamma=self.gamma,
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n_independent=self.n_independent,
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n_shared=self.n_shared,
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seed=self.seed,
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momentum=self.momentum,
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lambda_sparse=self.lambda_sparse,
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optimizer_params=self.optimizer_params,
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**kwargs
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n_d=self.n_d,
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n_a=self.n_a,
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n_steps=self.n_steps,
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gamma=self.gamma,
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n_independent=self.n_independent,
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n_shared=self.n_shared,
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seed=self.seed,
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momentum=self.momentum,
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lambda_sparse=self.lambda_sparse,
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optimizer_params=self.optimizer_params,
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**kwargs
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)
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self.model.fit(x_train, y_train, eval_set=[(x_valid, y_valid)])
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@@ -25,7 +25,9 @@ class BaseStrategy:
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return 0.95
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def generate_order_list(self, score_series, current, trade_exchange, pred_date, trade_date):
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"""Parameter
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"""
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Parameters:
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-----------
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score_series : pd.Seires
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stock_id , score
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current : Position()
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@@ -44,8 +46,8 @@ class BaseStrategy:
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def update(self, score_series, pred_date, trade_date):
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"""User can use this method to update strategy state each trade date.
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Parameter
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---------
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Parameters:
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-----------
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score_series : pd.Series
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stock_id , score
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pred_date : pd.Timestamp
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@@ -97,7 +99,7 @@ class AdjustTimer:
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Responsible for timing of position adjusting
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This is designed as multiple inheritance mechanism due to
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- the is_adjust may need access to the internel state of a strategyw
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- the is_adjust may need access to the internel state of a strategy
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- it can be reguard as a enhancement to the existing strategy
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"""
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@@ -139,7 +141,7 @@ class WeightStrategyBase(BaseStrategy, AdjustTimer):
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def generate_target_weight_position(self, score, current, trade_date):
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"""
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Parameters:
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---------
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-----------
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score : pred score for this trade date, pd.Series, index is stock_id, contain 'score' column
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current : current position, use Position() class
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trade_exchange : Exchange()
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@@ -228,7 +230,7 @@ class TopkDropoutStrategy(BaseStrategy, ListAdjustTimer):
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Gnererate order list according to score_series at trade_date, will not change current.
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Parameters:
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----------
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-----------
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score_series : pd.Series
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stock_id , score
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current : Position()
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